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MGBN: Convolutional neural networks for automated benign and malignant breast masses classification

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Abstract

Automated benign and malignant breast masses classification is a crucial yet challenging topic. Recently, many studies based on convolutional neural network (CNN) are presented to address this task, but most of these CNN-based methods neglect the effective global contextual information. Moreover, their methods do not further analyze the reliability and interpretability of CNN models, which does not correspond to the clinical diagnosis. In this work, we firstly propose a novel multi-level global-guided branch-attention network (MGBN) for mass classification, which aims to fully leverage the multi-level global contextual information to refine the feature representation. Specifically, the MGBN includes a stem module and a branch module. The former extracts the local information through standard local convolutional operations of ResNet-50. The latter embeds the global contextual information and establishes the relationships of different feature levels via global pooling and Multi-layer Perceptron (MLP). The final prediction is computed by local information and global information together. Then, we discuss the reliability and interpretability of our mass classification network by visualizing the coarse localization map through Gradient-weighted Class Activation Mapping (Grad-CAM), which is important in clinical diagnosis. Finally, our proposed MGBN is greatly demonstrated on two public mammographic mass classification databases including the DDSM and INbreast databases, resulting in AUC of 0.8375 and 0.9311, respectively.

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Acknowledgements

We would like to thank the Breast Research Group, INESC Porto, Portugal for the INbreast database. This work is jointly supported by the National Natural Science Foundation of China (Nos.61961037), Natural Science Foundation of Gansu Province (Nos.18JR3RA288), and the Fundamental Research Funds for the Central Universities (Nos.lzuxxxy-2019-tm23).

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Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, People’s Republic of China

    Meng Lou, Yunliang Qi, Wenwei Zhao, Chunbo Xu, Jie Meng & Yide Ma

  2. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People’s Republic of China

    Runze Wang

  3. College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, Gansu, People’s Republic of China

    Xiangyu Deng

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  1. Meng Lou
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Lou, M., Wang, R., Qi, Y. et al. MGBN: Convolutional neural networks for automated benign and malignant breast masses classification. Multimed Tools Appl 80, 26731–26750 (2021). https://doi.org/10.1007/s11042-021-10929-6

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